Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles (AV) are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about the driving environment. Studies have proved that automated driving have the potential to decrease traffic congestion by reducing the time headway (THW), enhancing the traffic capacity and improving the safety margins in car following. Despite different encouraging factors, automated driving raise some concerns such as possible loss of situation awareness, overreliance on automation and system failure. This paper aims to investigate the effects of AV on driver's behavior and traffic performance. A literature review was conducted to examine the AV effects on driver's behavior. Findings from the literature survey reveal that conventional vehicles (CV), i.e. human driven, which are driving close to a platoon of AV with short THW, tend to reduce their THW and spend more time under their critical THW. Additionally, driving highly AV reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of AV on traffic performance, a simulation case study consisting of a 100% AV scenario and a 100% CV scenario was performed using microscopic traffic simulation. Outputs of this simulation study reveal that the positive effects of AV on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably improved by 8.09% during p.m. peak hours in the AV scenario, while the average travel speed enhanced relatively by 8.48%. As a consequent, the average travel time improved by 9.00% in the AV scenario. The outcome of this study jointly with the previous driving simulator studies illustrates a successful practice of microscopic traffic simulation to investigate the effects of AV. However, further development of the microscopic traffic simulation models are required and further investigations of mixed traffic situation with AV and CV need to be conducted.

This article describes a framework for generation and simulation of surrounding vehicles in a driving simulator. The proposed framework generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between vehicles. The framework is based on an approach in which only a limited area around the driving simulator vehicle is simulated. This closest neighborhood is divided into one inner area and two outer areas. Vehicles in the inner area are simulated according to a microscopic simulation model including advanced submodels for driving behavior while vehicles in the outer areas are updated according to a less time-consuming mesoscopic simulation model. The presented work includes a new framework for generating and simulating vehicles within a moving area. It also includes the development of an enhanced model for overtakings and a simple mesoscopic traffic model. The framework has been validated on the number of vehicles that catch up with the driving simulator vehicle and vice versa. The agreement is good for active and passive catch-ups on rural roads and for passive catch-ups on freeways, but less good for active catch-ups on freeways. The reason for this seems to be deficiencies in the utilized lane-changing model. It has been verified that the framework is able to achieve the target flow and that * Swedish National Road there is a gain in computational time of using the outer areas. The framework has also been tested within the VTI Driving simulator III.

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